In this paper, aiming at the performance of intercalated meltblown nonwoven materials, the relationship between its process parameters, structural variables and product performance is explored, and the product performance can be optimized by changing the process parameters through regression, planning and other methods. The results of grey correlation analysis showed that the intercalation rate had a significant effect on both structural variables and product properties. According to the results of the multivariate nonlinear regression model, the ideal maximum cleaning efficiency can reach 99.7%. Finally, in order to explore when the influencing factors such as acceptance distance, hot air speed, thickness, and compression rebound rate are limited, to achieve the goal of achieving the highest filtering efficiency and the smallest filtering resistance as possible, the machine learning method is used, and random forest is selected as the regression model, build samples and make predictions, and finally get the optimal value of the acceptance distance of 21 cm and the hot air speed of 1580 r/min.